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BEHAVIORAL AND BRAIN SCIENCES (2017), Page 1 of 67 doi:10.1017/S0140525X16000959, e195

The evolution of general intelligence Judith M. Burkart Department of Anthropology, University of Zurich, CH-8125 Zurich, Switzerland [email protected] http://www.aim.uzh.ch/de/Members/seniorlecturers/judithburkart.html

Michèle N. Schubiger Department of Anthropology, University of Zurich, CH-8125 Zurich, Switzerland [email protected] http://www.aim.uzh.ch/de/Members/phdstudents/micheleschubiger.html

Carel P. van Schaik Department of Anthropology, University of Zurich, CH-8125 Zurich, Switzerland [email protected] http://www.aim.uzh.ch/de/Members/profofinstitute/vanschaik.html

Abstract: The presence of general intelligence poses a major evolutionary puzzle, which has led to increased interest in its presence in nonhuman animals. The aim of this review is to critically evaluate this question and to explore the implications for current theories about the evolution of cognition. We first review domain-general and domain-specific accounts of human cognition in order to situate attempts to identify general intelligence in nonhuman animals. Recent studies are consistent with the presence of general intelligence in mammals (rodents and primates). However, the interpretation of a psychometric g factor as general intelligence needs to be validated, in particular in primates, and we propose a range of such tests. We then evaluate the implications of general intelligence in nonhuman animals for current theories about its evolution and find support for the cultural intelligence approach, which stresses the critical importance of social inputs during the ontogenetic construction of survival-relevant skills. The presence of general intelligence in nonhumans implies that modular abilities can arise in two ways, primarily through automatic development with fixed content and secondarily through learning and automatization with more variable content. The currently best-supported model, for humans and nonhuman vertebrates alike, thus construes the mind as a mix of skills based on primary and secondary modules. The relative importance of these two components is expected to vary widely among species, and we formulate tests to quantify their strength. Keywords: brain size evolution; comparative approach; cultural intelligence; evolution of intelligence; general intelligence; modularity; nonhuman primates; positive manifold; psychometric intelligence; rodents; social learning; species comparisons

1. Domain-general and domain-specific accounts of human cognition “Animal behavior is driven by instincts, whereas human beings behave rationally.” Views like these are still commonly expressed and deeply anchored in the Western worldview (e.g., Pinker 2010). A modern version of this dichotomy construes animals as having domain-specific, modular cognitive adaptations, whereas humans have domain-general intelligence. However, we now know that in human cognition, domain-specific components are ubiquitous too (Cosmides & Tooby 2013), perhaps even in complex cognitive tasks such as logical inference (Cosmides et al. 2010) or solving Bayesian probability problems (Lesage et al. 2013). At the same time, much evidence has accumulated that nonhuman minds are not exclusively made up of domain-specific specializations, but that domain-general cognitive processes may also be widespread. These empirical findings have implications for contemporary theories of the evolution of general intelligence, highlighted in section 3, provided it is established that general intelligence in animals is both real and refers to the same construct as in humans. © Cambridge University Press 2017

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JUDITH M. BURKART is a senior researcher at the Department of Anthropology of the University of Zurich. She leads the Evolutionary Cognition Group and is interested in the cognitive evolution of primates. A main focus of the group concerns the role of systematic allomaternal care, present in callitrichid monkeys and humans, in the evolution of social, motivational, and cognitive processes. MICHÈLE N. SCHUBIGER is a Ph.D. student of the Evolutionary Cognition Group, University of Zurich, and investigates general intelligence in marmoset monkeys. Currently, she is also a fellow in psychology, primate cognition, at Abertay University, Dundee, studying cognitive abilities in gibbons. CAREL P. VAN SCHAIK is Professor and Director of the Department of Anthropology, University of Zurich. His main interests are socioecology and social evolution in primates, especially the primate foundations of human culture and intelligence. He wrote Among Orangutans: Red Apes and the Rise of Human Culture (Belknap Press of Harvard University Press, 2004) and the recent textbook, Primate Origins of Human Nature (Wiley-Blackwell, 2016), and has co-edited various volumes on primate behavior and conservation.

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Burkart et al.: The evolution of general intelligence The evolution of general intelligence poses a major puzzle. Because modular systems may readily evolve (Pavlicev & Wagner 2012; Schlosser & Wagner 2004; Shettleworth 2012b; but see Anderson & Finlay 2014; Lefebvre 2014), the evolution of the mind as a set of domain-specific adaptations or modules can easily be imagined. Indeed, a small set of dedicated modules, without any domaingeneral cognitive abilities, to which additional modules can be added as needed, may be the ancestral state of vertebrate cognition. This perspective is so convincing that it has led to accounts of massive modularity, not only for animal cognition, but also for human cognition as well (reviews: Barrett 2015; Frankenhuis & Ploeger 2007; Hufendiek & Wild 2015). Evolutionary pathways leading to the emergence of domain-general cognitive processes, on the other hand, may appear less straightforward, because such open-content processes translate far less reliably into fitness-enhancing behavior, and because they may also require disproportionate amounts of energetically costly brain tissue compared to domain-specific specializations (van Schaik et al. 2012). Consequently, compared to the evolution of additional cheap and reliable, domain-specific, specialized cognitive solutions to specific problems, the evolution of general cognitive processes might pose greater obstacles to natural selection. Nonetheless, humans possess general intelligence, and if general intelligence can also be found in nonhuman animals, we can attempt to identify the evolutionary processes that can lead to its emergence, including the specific case of humans. The aims of this review are (1) to critically evaluate the evidence for general intelligence in nonhuman animals, and (2) to explore the implications of its presence in nonhumans for current theories of cognitive evolution. To achieve these aims, we will review the theoretical background and evidence from a variety of research traditions, such as animal behavior and psychology, psychometrics and developmental psychology, and evolutionary psychology. Whereas all of these fields share an interest in understanding how the mind works, they are not well integrated, and attempts at integration have not yet produced consensus (e.g., Eraña 2012; Evans 2011; 2013; Toates 2005). In this target article, we will therefore selectively focus on those aspects that are necessary to integrate the findings from animal studies on general intelligence with what is known about humans. As non-experts in several of these fields, we are aware that we may not fully represent all of the relevant aspects of the respective theories, let alone solve current controversies in individual fields. Nevertheless, we hope that this article serves as a first step in achieving the much-needed integration across these disciplines at a more fine-grained level, which will eventually enable the development of a more unified theory of cognitive evolution. This article is structured as follows. We first briefly review conceptualizations of both domain-generality and domain specificity of human cognition, and use this as background to situate current evidence for general intelligence in nonhuman animals, which is increasingly reported in various species based on factor-analytical approaches. We examine alternative explanations for these findings and develop a set of empirical criteria to investigate to what extent a statistically derived psychometric factor does indeed correspond to general intelligence as broadly defined. Such criteria are 2

increasingly met in rodent studies but are strikingly underexplored in primates or birds. Next, we discuss different evolutionary theories that may explain why and how general intelligence can be widespread in nonhuman animals even though it is not immediately obvious how it can reliably produce fitness-enhancing behavior. We argue that the broad version of the cultural intelligence approach (Tomasello 1999; van Schaik & Burkart 2011; van Schaik et al. 2012) can best account for the current body of evidence. We end by proposing a model that construes the mind of both humans and nonhuman vertebrates as a mix of truly modular skills and seemingly modular skills that are ontogenetically constructed using general intelligence abilities. We refer to them as primary and secondary modules, respectively. Species differences are likely with regard to the importance of these components, and we formulate tests to quantify their strength. 1.1. The positive manifold and general intelligence

Intelligence in humans has been intensely studied for more than a century (e.g., reviewed in Deary et al. 2010; Nisbett et al. 2012). It is broadly defined as involving “the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is thus not merely book learning, a narrow academic skill, or test-taking smarts. Rather it reflects a broader and deeper capability for comprehending our surroundings – ‘catching on,’ ‘making sense’ of things, or ‘figuring out what to do’” (Gottfredson 1997, p. 13). This definition has received broad acceptance (Nisbett et al. 2012). In animals, intelligence is thought to involve an individual’s ability to acquire new knowledge from interactions with the physical or social environment, use this knowledge to organize effective behavior in both familiar and novel contexts, and engage with and solve novel problems (Byrne 1994; Rumbaugh & Washburn 2003; Yoerg 2001). Thus, general intelligence, as defined in either humans or nonhuman animals, stresses reasoning ability and behavioral flexibility. The concept of human general intelligence is built on one of the most replicated findings in differential psychology. In humans, performance across tasks of different cognitive domains is positively correlated: the positive manifold. Factor-analytical procedures applied to large data sets of individual performance across tasks consistently reveal a single factor that loads positively overall and can explain a significant amount of variation, often termed g for (psychometric) general intelligence. Within this psychometric, factor-analytical approach, an individual’s loading on this factor thus estimates its intelligence. Performance in specific cognitive tasks (e.g., Raven’s Progressive Matrices) or test batteries (e.g., Wechsler Adult Intelligence Scale [WAIS]) is highly correlated with g, and is in fact often used as a proxy measure for it, for instance in studies aimed at localizing g in the brain (Burgess et al. 2011; Colom et al. 2006; Gläscher et al. 2010). In this article, we will speak of general intelligence when referring to the broad definition of Gottfredson (1997) that stresses reasoning ability and behavioral flexibility, and of psychometric intelligence when referring to the entity estimated by the psychometric variable g. For humans, it is generally assumed that g estimates general intelligence, based on the strong empirical correlations between the two, as reviewed below.

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Burkart et al.: The evolution of general intelligence Psychometric intelligence, estimated through g, typically explains around 40% of variance in test performance, whereas the rest is explained by group factors and variance unique to specific tasks (Plomin 2001). It has been found that g in humans has a clear genetic foundation (Davies et al. 2011), and in the absence of adverse environments that compromise the complete deployment of an individual’s capacity, heritability can explain remarkably high proportions of variance (Joshi et al. 2015; Nisbett et al. 2012). Furthermore, g has robust correlates in brain structure and function, such as brain size, gray matter substance, cortical thickness, or processing efficiency (Deary et al. 2010; Jung & Haier 2007). However, rather than being localized in specific brain parts, it seems to be a systemlevel property of the brain (Pietschnig et al. 2015). Finally, g is also a good predictor for various measures of life outcome, including school achievement, the probability of being in professional careers, occupational attainment, job performance, social mobility, and even health and survival. In particular, it is better at predicting such variables than specific cognitive abilities on their own (reviewed in Deary et al. 2010; Reeve 2004). 1.1.1. The structure of cognition. The structure of human

cognition continues to be debated (e.g., Ortiz 2015). Nonetheless, the presence of g is now widely accepted due to the pervasive evidence from Carroll’s (1993) seminal metaanalysis of over 460 carefully selected data sets on human cognitive ability. An influential account is Horn and Cattell’s fluid-crystallized gf-gc model (see also Major et al. 2012). Fluid intelligence gf refers to the capacity to think logically and solve problems in novel situations independently of previously acquired knowledge, and to identify patterns and relationships, whereas crystallized intelligence gc refers to the ability to use skills, knowledge, and experience and crucially relies on accessing information from long-term memory. An explicit causal link from gf to gc is provided by investment theory (Cattell 1987), which is the developmental version of the gf-gc model and finds considerable empirical support (Thorsen et al. 2014). An integrated version, the so-called CHC (Cattell-HornCarroll) theory, has been supported by several studies and is a widely accepted consensus model (McGrew 2009). The CHC model is hierarchical, placing a general factor g at the top, which affects both gf and gc. Most current models involve some hierarchical structure involving a general factor, g, and fluid intelligence, gf (but see, for instance, Bartholomew et al. 2009; Major et al. 2012; van der Maas et al. 2006). In fact, some have argued that gf and g represent the same entity (Kan et al. 2011), and the previously mentioned definition of intelligence in a broad sense in fact emphasizes elements of both constructs. Some models of general intelligence that do not involve g are also still being considered. Van der Maas et al. (2006), for instance, have presented a dynamic model of general intelligence that assumes independent cognitive processes early in ontogeny. Over the course of development, the positive manifold emerges because of mutually beneficial interactions between these initially independent processes. To the extent that one agrees to equate general intelligence with the positive manifold, the mutualism model may be viewed as a model of general intelligence for human and nonhuman animals in which variation between species would reflect the extent to which mutually beneficial

interactions between cognitive processes arise during development. Because, across species, bigger brains require more time to mature than smaller brains (Schuppli et al. 2012), and thus have more opportunities to develop such mutually beneficial interactions, such a scenario is compatible with an evolutionary perspective. 1.1.2. Executive functions and intelligence. Closely related to general intelligence are executive functions, or EFs (Barbey et al. 2012; Blair 2006). EFs refer to “generalpurpose control mechanisms that modulate the operation of various cognitive subprocesses and thereby regulate the dynamics of human cognition” (Miyake et al. 2000, p. 50). In other words, they are “a family of top-down mental processes needed when you have to concentrate and pay attention, when going on automatic or relying on instinct or intuition would be ill-advised, insufficient, or impossible” (Diamond 2013, p. 136). Three core EFs can be distinguished, namely inhibitory control (behavioral inhibition, cognitive inhibition, and selective attention), working memory (Baddeley 2010), and cognitive flexibility. Various measures of EFs have shown strong correlations with g/gf. Whereas the average correlation between working memory and g is 0.72, in some studies using latent variable analysis, it even reached identity (Colom et al. 2005; Nisbett et al. 2012), leading some authors to suggest that the two cannot be distinguished from each other (Royall & Palmer 2014). That g and EF are closely related is consistent with two further lines of evidence. First, working memory can be trained, and these training gains can translate into gains in general intelligence even though not all procedures are effective, and it is not always clear whether the training affects working memory per se or instead improves learning strategies (reviewed in Klingberg 2010; Morrison & Chein 2011; Nisbett et al. 2012; Shipstead et al. 2012). Second, growing up bilingually, which makes high demands on a variety of EFs on a routine basis, is associated with stronger EFs in non-linguistic contexts, and thus with g (Abutalebi & Clahsen 2015; Bialystok et al. 2012; Rabipour & Raz 2012). Nonetheless, because EFs do not provide the logical problemsolving functions and learning that are the hallmark of general intelligence (Embretson 1995), some aspects of general intelligence are independent of EFs. In sum, evidence for domain-general intelligence in humans, estimated by the first factor derived in psychometric, factor-analytical approaches, is pervasive, and is backed up by neurobiological evidence and various correlates of life-outcome measures. The psychometrically derived g factor is thus consistent with the broad notion of general intelligence, which stresses reasoning ability and behavioral flexibility and invokes cognitive processes such as learning and remembering, planning, and executive functions. This conclusion raises the question of the evolutionary origin of general intelligence in humans, which we will address by reviewing recent developments in the nonhuman literature. To do so, we will review evidence for g in animals, and whether it is warranted to assume that g in animals is also consistent with a broader notion of general intelligence. Intelligent behavior needs to be distinguished from behavior that may appear intelligent but lacks flexibility (Shettleworth 2012a). Intelligent behavior in animals is often referred to as behavior that shows some degree of flexibility and emanates from some kind of mental

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Burkart et al.: The evolution of general intelligence representation rather than immediate perception only (Tomasello & Call 1997). For instance, when digging wasps are interrupted anywhere in the sequence of actions involved in measuring the size of a hole to place a larva together with a prey item into it, they must start again at the very beginning of the behavioral sequence (Wooldridge 1968). Thus, many behaviors that at first sight look like they are the product of reasoning or learning turn out to be inflexible adaptations or modules (Sherry 2006). A collection of such dedicated adaptations presumably represents the ancestral state (e.g., Shettleworth 2012a; 2012b), and thus the null model against which the hypothesis of general intelligence has to be tested. Before turning to nonhuman animals, we will therefore provide an overview of domain-specific, modular conceptions of the mind that have been put forward particularly, but not exclusively, by evolutionary psychologists. 1.2. Cognitive adaptations and domain specificity

A domain-general factor of intelligence can be contrasted with domain-specific cognitive mechanisms or adaptive specializations (Cosmides & Tooby 2002). The basic idea is that whenever a fitness-relevant cognitive problem arises repeatedly and predictably over long periods of time in a given species, natural selection favors a genetically based, developmentally canalized (“hardwired”) solution to this problem. For instance, natural selection may provide a species with a particularly strong spatial memory to retrieve stored food, without endowing it with more-powerful cognitive capacities in other contexts (Sherry 2006). Importantly, domain-specific mechanisms cannot be used in domains other than the ones for which they evolved, whereas domain-general mechanisms can be used to solve problems across domains. Thus, the mind of animals, including humans, can be conceived of as a collection of adaptive specializations, often construed as modules, each of which evolved to solve a specific adaptive problem (Duchaine et al. 2001). Notice that a mind uniquely made up of these kinds of specific adaptations is arguably incompatible with standard accounts of intelligence, because virtually no learning and flexibility are involved. Similarly, none of these specific cognitive adaptations require the presence of the domain-general processes underlying intelligence such as executive functions. 1.2.1. Modularity and general intelligence. A modular organization of mind is particularly appealing to evolutionary thinking because modular systems allow parts to be removed, added, or modified without affecting the function of the structure as a whole. Therefore, modular systems may be more evolvable or even the only evolvable systems (Clune et al. 2013; Pavlicev & Wagner 2012; Ploeger & Galis 2011; Schlosser & Wagner 2004; Shettleworth 2012b). Thus, whenever conditions are sufficiently stable or at least predictable across generations, natural selection should favor solving recurrent fitness problems via modules rather than via general cognitive processes, because the former solve these problems on average quickly, effortlessly, and efficiently (Cosmides et al. 2010) and can presumably evolve more readily. General intelligence, in contrast, is thus expected to evolve under conditions of social or environmental unpredictability. Solutions to these evolutionarily novel problems have to

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be acquired effortfully, via slow learning (e.g., Geary 2005; Geary & Huffman 2002). The advantages of a modular solution to recurrent fitness problems, however, are not necessarily as straightforward. First, the fundamental assumption that a modular solution is indeed more evolvable can be questioned on both empirical and conceptual grounds (e.g., Anderson & Finlay 2014; Bolhuis et al. 2011; d’Souza & Karmiloff-Smith 2011; Lefebvre 2014). Empirical evidence for a direct mapping of specialized adaptive behavioral functions to specific modular neural units is actually rare, even for neural systems as simple as those of invertebrates. Novel adaptive functions seem mostly to be achieved via massive re-use of neural tissue rather than via the addition of encapsulated neuronal pools. Conceptually, the evolvability argument seems largely incompatible with what is known about short-term neuromodulation, brain plasticity over the life span, response to damage, and ontogenetic principles of brain development. The a priori evolvability argument, therefore, does not lead to an unambiguous conclusion as to the superiority of domain-specific over domain-general organization. Second, the other advantage of modularity – fast, effortless, and ultimately efficient solving of evolutionarily recurrent fitness problems – may hold only for particular notions of modularity, such as Fodorian modules (Fodor 1983). These are thought to be domain-specific functional units that process distinctive input stimuli using distinctive mechanisms. In particular, a module is thought to exclusively process information from a specific domain and to produce a correspondingly specific output in the form of representations and/or a behavioral response. Fodor listed criteria that must – at least to “some interesting extent” (Fodor 1983, p. 37) – be fulfilled by a functional unit to qualify as modular. These criteria include domain specificity, mandatory processing, high speed, production of shallow outputs (i.e., not requiring extensive processing), limited accessibility, a characteristic ontogeny (reliable emergence without explicit learning), a fixed neural architecture, and informational encapsulation (meaning it is not affected by other cognitive processes, a criterion thought to be particularly important). Paradigmatic examples of Fodorian modules are optical illusions. Accordingly, the presence of modules involving the processing of sensory information is widely accepted, and that their speed and efficiency are beneficial is obvious. However, a modular organization has also been proposed for more higher-level cognitive processes including ones related to folk psychology (e.g., processing of faces and facial expressions, theory of mind, cheater detection), folk biology (e.g., animate-inanimate distinction, flora-fauna), or folk physics (e.g., movement trajectories, gravity biases, representation of space, solidity, and causality; summarized in Geary 2005). Indeed, massive modularity accounts hold that the mind is exclusively made up of modules (Barrett 2015; Carruthers 2005; Sperber 2001). Massive modularity would appear to be irreconcilable with general intelligence (and therefore with the ability to solve evolutionarily novel problems), but much of the longstanding controversy about the massive modularity hypothesis of the human mind comes down to the use of different notions of modularity (see also Barrett & Kurzban 2006). Indeed, a variety of highly divergent notions have developed (Barrett 2015; Barrett & Kurzban 2012; Chiappe &

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Burkart et al.: The evolution of general intelligence Gardner 2012; Coltheart 2011; Grossi 2014; Mahon & Cantlon 2011), and many of these are much broader than the Fodorian one (e.g., Sternberg 2011). Because they also encompass the possibility of overarching, central control processes (Carruthers 2011), they are entirely compatible with the coexistence of domain-general processes and general intelligence (Barrett 2015; Carruthers 2011). In fact, Carruthers (2011) argued that most modules are specialized learning systems. Such broad notions of modularity, however, arguably no longer support the original idea of automatically providing fast and frugal solutions to recurrent fitness problems. Unlike many proponents of massive modularity in humans, comparative behavioral biologists and comparative psychologists typically refer to notions of modularity that hew closely to the classical Fodorian modules, that is, dedicated, inflexible cognitive adaptations that have evolved in response to specific recurrent fitness-relevant problems (e.g., Fernandes et al. 2014; Shettleworth 2012a; 2012b). Functional specialization here is mostly used in the biological, ultimate sense – that is, referring to the specific adaptive pressures that gave rise to the evolution of specific dedicated modules. This perspective is grounded in research traditions such as neuroecology (Sherry 2006) that have provided empirical evidence for the occurrence among animals of dedicated cognitive adaptations, such as spatio-temporal memory abilities in food-caching species, birds in particular (Brodin 2010; Pravosudov & Roth 2013). These cognitive adaptations typically do not generalize to problems for which they did not evolve. A mind composed of such dedicated adaptations represents a plausible null model, and indeed a plausible ancestral state of vertebrate cognition. Dedicated adaptations and general intelligence can obviously coexist (e.g., Cosmides et al. 2010; Geary 2005) – for instance, when the output of modules serve as inputs for intelligent reasoning, which may be responsible for the fact that in humans general intelligence predicts reasoning ability even in evolutionarily familiar contexts (Kaufman et al. 2011). The key questions with respect to the evolution of general intelligence, therefore, are how central, domain-general processes could evolve on top of domain-specific adaptations, whether and to what extent they also exist in nonhuman animals, and what adaptive benefits drove their evolution. 1.2.2. Adaptive canalization beyond modularity. Strictly domain-general approaches that construe the mind as a general-purpose computer face several well-known problems (Cosmides & Tooby 1994; Cosmides et al. 2010; Frankenhuis & Ploeger 2007; Heyes 2003; Kolodny et al. 2015; see also Table 1). First, an agent has to efficiently identify relevant information and filter out irrelevant information in the process of problem solving, a challenge known as the frame problem. Second, once the relevant information has been identified, the agent has to decide what to do with it. To do so, she has to solve the problem of how to pick and combine correct, adaptive behavioral options or cognitive processes out of an exponentially growing number of possibilities (the problem of the combinatorial explosion) or to learn important associations and skills in a limited period of time despite dealing with relevant stimuli that occur at a low rate (the poverty of the stimulus problem). Third, correct responses have to be made quickly and efficiently (the urgency problem). And

fourth, while doing so, the agent has to find general, rather than only locally successful, solutions (the functionality problem). It is thus beyond doubt that some canalization of cognitive processes is necessary. Evolved Fodorian modules (referred to as “cognitive adaptations” by behavioral biologists and neuroecologists) are clearly one way of solving the problems highlighted previously, in particular when they define the entire sequence from the acquisition of information to the adaptive behavioral response. However, they are not necessarily the only possible way, and natural selection may also overcome these problems in a different way that would allow domaingeneral abilities to evolve. A straightforward solution to this problem would be that domain-general abilities coevolve together with adaptive canalizing mechanisms that guide how general abilities are applied. Canalizing mechanisms can have a phylogenetic origin, such as a genetically predetermined preference for a certain category of stimuli: for example, the preference for faces in human infants (Shah et al. 2015). Alternatively, they can have an ontogenetic origin, such as the propensity of chimpanzees from toolusing communities to automatically perceive a stick as a potential tool, compared to genetically indistinguishable chimpanzees from non-tool-using communities who do not recognize this affordance (e.g., Gruber et al. 2011). Table 1 summarizes the phylogenetic and ontogenetic canalizing mechanisms that ensure that domain-general cognition produces adaptive behavior despite the problems highlighted previously. Unlike Fodorian modules, these mechanisms do not define the entire sequence from signal detection to behavioral output, but may be deployed at different stages during information processing. We will now examine the evidence for such domain-general canalization processes. The first problem an individual faces is what to attend to in the continuous stream of stimuli coming in from different sensory modalities. This can be solved by innate dispositions or data acquisition mechanisms (also referred to as phylogenetic inflection: Heyes 2003). Importantly here, innateness is not equivalent to inflexibility because innate dispositions to pay attention to one stimulus over another can be conditional. For instance, an animal foraging for berries may have an attentional bias to perceive small red entities, but the same animal when exposed to a raptor will be biased to perceive only potential hideouts. Alternatively, animals can learn ontogenetically which targets are particularly worth attending to (ontogenetic inflection). Here, social guidance of attention may play a particularly important role. Ontogenetic inflection automatically arises whenever immatures follow the mother and later other conspecifics, and is even more powerful in species that follow gaze (Shepherd 2010). In many species, including humans, immatures are particularly attracted to everything conspecifics are interacting with, and immatures of some species, such as aye-ayes (Krakauer 2005), marmoset monkeys (Voelkl et al. 2006), or orangutans (Forss et al. 2015) are highly neophobic toward stimuli they have not witnessed their mother or other familiar conspecifics interact with. Natural selection can, therefore, favor the disposition to preferentially use social information to decide which stimuli to attend to, and thus leave the specific target of attention largely unspecified. In a second step, the individual has to “decide” what to do with the stimuli that have captured its attention, 5

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Burkart et al.: The evolution of general intelligence Table 1. Overview of some specific problems that a domain-general cognitive apparatus has to overcome in order to produce ultimately adaptive behavior, as well as potential solutions – that is, adaptive canalization mechanisms. Note that these solutions may be very general themselves, such as a preference for social learning. See text for references. Problem

Domain-General Canalization Processes

Examples

The frame problem: What to attend to?

Input filters (phylogenetic inflection)

Facilitated detection of small red entities (when hungry) or dark openings (when chased) Immatures following mothers, or following mothers’ gaze Flight reactions, learning to be fearful of snakes but not flowers

Problems of combinatorial explosion and poverty of stimulus What to do with the information?

Socially guided attention (ontogenetic inflection) Direct triggering, prepared learning

Socially guided learning Integration with core knowledge1

The urgency problem: How to reach a quick, efficient response?

The functionality problem: How to find generally, not only locally, successful solutions?

Innate response tendencies Acquired response tendencies (automatization, secondary modules) Innate goals

Socially acquired end-state preferences

Copying how to extract food from a matrix Embedding the expectation that objects always fall down in a straight line (gravity bias) with knowledge of solidity Evolved modules, evolved heuristics (primary modules) Learned heuristics to solve algebraic equations (secondary modules) Innate template of a safe burrow, or of good food Learning by following mother what a good sleeping place is; copying the goals of successful individuals, conformity biases

That is, evolved cognitive domains that are fleshed out with experience; for example, Gelman (1990).

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because input mechanisms filter incoming stimuli but do not produce behavior. Subsequent processes are therefore required to determine what to do with these stimuli without being stymied by the problems of poverty of stimulus and the combinatorial explosion. First, in the case of phylogenetic inflection, coevolution of input mechanisms and response tendencies is frequent (Lotem & Halpern 2012), as when a moving stimulus in the sky automatically triggers a flight reaction, but also when individuals are more likely to associate a snake (but not a flower) with fear (Cook & Mineka 1989), or a taste (but not an auditory stimulus) with subsequent nausea (known as biologically prepared learning or the Garcia effect: Garcia & Koelling 1966). Second, in the case of ontogenetic inflection, social learning can also affect how the individual processes a stimulus that has come to its attention. Third, the stimuli that have attracted an individual’s attention may be integrated with innate bodies of knowledge, so-called core knowledge (Gelman 1990; Spelke & Kinzler 2007) or psychological primitives (Samuels 2004), and so give rise to more elaborate skills and conceptual systems (Carey 2009). A third problem for the individual is that decisions often have to be made under time pressure (the urgency problem). Evolved modules, heuristics, or direct and reflexive triggering of responses are particularly good at providing fast responses because they bypass central processes. But quick and efficient responses can also be achieved in evolutionarily novel contexts, such as solving algebraic equations or playing chess, if a learned heuristic approach becomes an automated subroutine and can be applied effortlessly (Bilalić et al. 2011; Chang 2014). Such problem solving 6

has similar surface properties to modular organization sensu Fodor. This fact has sometimes led to conceptual misunderstandings (see also section 1.2.3), and is relevant for approaches that try to identify domain-general processes in nonhuman animals (see also section 2.4.3). A final potential problem is that developmentally acquired response tendencies may be successful in solving local problems, but nevertheless may not ultimately help an individual survive and reproduce (the functionality problem). Individuals, be they animals or humans, typically do not represent ultimate fitness goals in their everyday behavior. Rather, they pursue a set of innate psychological goals, which on average results in fitness-enhancing behaviors (Tinbergen 1963) but may become maladaptive in environments other than the one in which the goals evolved, as shown by our strong preferences for sweet, fatty, and salty foods. However, innate goals may be modified or supplemented by socially acquired end-state preferences. For immatures, who are most strongly affected by the canalization problems listed in Table 1, copying successful adult individuals is widespread and generally results in adaptive behavior because they are copying individuals who have survived until adulthood and managed to reproduce. Socially acquired end-state preferences and goals are particularly widespread in humans, who are highly susceptible to conformity and prestige biases (Dean et al. 2014; Richerson et al. 2016). Increasing evidence also suggests the existence of such biases in at least some nonhuman primates and birds (Aplin et al. 2015; Kendal et al. 2015; Luncz & Boesch 2014; van de Waal et al. 2013).

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Burkart et al.: The evolution of general intelligence Despite being incomplete, Table 1 serves to highlight that adaptive canalization of cognition not involving Fodorian modules is possible, indeed potentially quite frequent. It also highlights the prominent reliance on social inputs to overcome the canalization problems inherent to domain-general mechanisms. Social learning is broadly defined in the animal literature – that is, learning influenced by observation of, or interaction with, another animal or its products (Heyes 1994; see also Box 1984). It is widespread in the animal kingdom, both in vertebrates and invertebrates, and ranges from processes as simple as social facilitation and enhancement learning to observational forms of social learning such as true imitation (e.g., Hoppitt & Laland 2013). Interestingly, it is increasingly assumed that many of the cognitive mechanisms involved in social learning are of a general nature rather than specialized, and are thus not specific to social learning (Behrens et al. 2008; Heyes 2012; 2016). Indeed, all forms of social learning also include a major element of individual learning. This is most evident in forms such as stimulus enhancement, where the attention of a naïve individual is drawn to stimuli other individuals are interacting with, which then releases individual exploration, play, and trial-and-error learning with this stimulus. Individual learning and practice, however, are also involved in the acquisition of skills through imitation learning, whereby it is typical that, after observation, a phase of individual practice is required (Galef 2015; Jaeggi et al. 2010; Schuppli et al. 2016). Thus, natural selection for social learning seems to automatically trigger selection on individual learning and general cognitive ability, suggesting that ontogenetic canalization through social learning may have contributed to enabling the evolution of domain-general cognition, an issue to which we return in section 3.3. 1.2.3. Primary and secondary modularization, and implications for general intelligence in nonhuman animals. Evolved Fodorian modules have specific surface

properties: they work fast, effortlessly, and automatically, and they do not require significant amounts of executive

control and working memory. Nevertheless, identifying modules in animals based on these properties is problematic because skills, capabilities, and solutions to problems that are acquired through effortful problem solving and learning based on general cognitive processes may become automatized over time, a process we refer to as secondary modularization. After such secondary modularization, or automatization, these skills have many of the surface properties in common with primary, evolved Fodorian modules. Note that this distinction in primary and secondary modularization is analogous to the distinction in primary and secondary cognitive abilities by Geary (1995), but whereas the latter has been developed specifically for humans, the former is thought to apply to a broad array of animal species. Despite the similarities in surface properties, primary and secondary modules differ fundamentally with regard to their origin (see Table 2): Primary modules are evolved adaptations with canalized, buffered development, whereas secondary modules represent ontogenetically acquired skills that were automatized during ontogeny. In fact, secondary modularization is particularly common during the immature period (d’Souza & Karmiloff-Smith 2011). A consequence of the different etiology of primary and secondary modules is that the latter are more variable in their content and distribution across individuals or populations of the same species. Because little is known about the ontogeny of many of the specialized cognitive modules postulated for humans (Geary 2005), we should also acknowledge the possibility that some or all of these are secondary rather than primary (Anderson & Finlay 2014) or at least subject to experiential influences. For instance, even some prototypical modules such as those involved in face perception depend on experience (Dahl et al. 2014). The implication for the question of general intelligence in nonhuman animals is that it is no longer possible to uniquely rely on surface properties such as speed, effort, efficiency, and reliability to infer the presence of evolved domain-specific modules, because secondary modules have similar properties. Instead, a better diagnostic tool

Table 2. Primary and secondary modules differ with regard to their etiology and development, which has implications for their content and distribution within a species or population Type of Module

Etiology

Primary modules

Evolutionary; reflect Skill matures, natural selection motor practice for domain(experiencespecific cognitive expectant1) adaptation

Secondary modules Ontogenetic; reflect behavioral flexibility and learning ability, acquisition often based on EFs

Development

Skill is learned (experiencedependent1) and practiced to the point of automaticity

Content of Skills

Distribution

Preset, highly predictable

Uniformly present in Tendency of (young) a given species felids to respond to small moving objects with behaviors from the hunting repertoire Automatic Variable among perception of a individuals, stick as potential populations tool in some apes; learned algorithms to solve algebraic equations in humans

More variable, determined by nature of inputs

Examples

1

Greenough et al. (1987).

7

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Burkart et al.: The evolution of general intelligence for the presence of general cognitive abilities is the presence of variable skill profiles across individuals and genetically similar populations due to secondary modularization (see section 2.4.3). We have shown that human cognition involves elements of domain-specific and domain-general processes, but that the same can potentially be true for animals as well. Hence, animal minds need not be bundles of specialized cognitive adaptations. Having thus leveled the playing field, we first, in section 2, review recent evidence for whether a positive manifold (g) is present in nonhuman animals at all, and if so, how such a g factor is best explained. In particular, we will focus on the question whether such psychometric intelligence shows any of the features usually referred to as general intelligence. Even if we can be confident that this is the case in humans, whether the same applies to animals must be an empirical question (Galsworthy et al. 2014), and we highlight different research strategies that may prove to be fruitful in the future. In section 3, we then use this pattern of results to examine the ultimate evolutionary question of why general intelligence evolved, and which selection pressures may have favored it. 2. General intelligence in nonhuman animals? Unless general intelligence is inextricably linked to language, considerations of evolutionary continuity suggest that nonhuman animals, especially our closest extant relatives, the great apes, may well possess it too, at least to some extent. The presence of evidence for executive functions in animals (Chudasama 2011) supports this contention, as does the overall flexibility of brains in animals, both during development and as response to experience, including the training of cognitive skills (Johansen-Berg 2007; Kolb & Gibb 2015; Matsunaga et al. 2015; Sale et al. 2014). According to most neurobiologists, such developmental plasticity is incompatible with purely domainspecific descriptions of cognitive abilities (Anderson & Finlay 2014; Prinz 2006; Quartz 2003). Nonetheless, evolutionary plausibility does not amount to empirical evidence, to which we turn now. The question of whether general intelligence is unique to humans has typically been addressed by asking whether we find a positive manifold or psychometric intelligence, by following two complementary approaches: First, within a given species, in analogy to human studies, psychometric test batteries have been applied to many individuals. Second, broad comparative analyses (both experimental and meta-analytical) have been conducted across species to investigate whether species differ from each other in general intelligence, rather than in specific cognitive adaptations. In addition, some studies have simultaneously analyzed intraspecific and interspecific variation in cognitive performance. In the following subsections, we first give an overview of these studies. We refer to general factors extracted from intraspecific studies as g, and to those extracted from interspecific studies as G. We then critically assess to what extent alternative explanations may account for the findings, and formulate criteria for future studies that should help pin down to what extent a statistically derived g/G factor reflects general intelligence as broadly defined. 8

2.1. Intraspecific studies of psychometric intelligence: g

Interest in the question of whether general intelligence may be found in nonhuman animals briefly spiked in the 1930s and 1940s (Locurto & Scanlon 1998), after Spearman’s g factor (Spearman 1927) had become widely known. These studies reported positive correlations across various types of tasks, but predominantly concerned mazes and mostly in non-primate species such as mice, rats, and chicks (Locurto 1997). Because the model of a hierarchical structure of human cognition and the methodological tools to detect it became widely available only in the late 1940s, the design of these early studies was often not suitable to detect g or any factor structure. For the next half century, the question of animal general intelligence was largely ignored, with interest resurging only after the late 1990s, mainly focusing on mice and primates. Table 3 provides an overview of these studies that have assessed and analyzed correlated performance across three or more cognitive tasks within subjects of the same species, for rodents, primates, and other species (see also Bouchard 2014; Chabris 2007; Galsworthy et al. 2014; Matzel et al. 2013). In rodents, robust evidence for g is available from a range of studies, mostly on mice, from test batteries including as many as eight different tasks and various regimes of principal component analysis (e.g., reviewed in Bouchard 2014; Galsworthy et al. 2014; Matzel et al. 2011b; but see Locurto et al. 2003; 2006). In general, g explains between 30% and 40% of variation in cognitive performance, and in rats, it is positively correlated with brain size (Anderson 1993). Moreover, heritability estimates of up to 40% have been reported (Galsworthy et al. 2005). Test batteries often include typical, rather basic learning tasks, such as associative fear conditioning, operant avoidance, path integration, odor discrimination, and spatial navigation. Nevertheless, as in humans, the derived g factors have been shown to covary with executive functions, such as selective attention (Kolata et al. 2007; Matzel et al. 2011a) and working memory (particularly working memory capacity: Kolata et al. 2005; Matzel et al. 2008; Sauce et al. 2014) as well as performance in tests of reasoning. For instance, g derived from a standard mouse test battery predicted performance in inductive (finding efficient search strategies in a complex maze) and deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., “fast mapping”: Wass et al. 2012). Working memory training did increase g (Light et al. 2010; Matzel et al. 2011a), mainly through its positive effect on selective attention (Light et al. 2010; see also Sauce et al. 2014). Importantly, g did not simply capture fear and stress reactivity (Matzel et al. 2006), anxiety (Galsworthy et al. 2002), or other lower-level biological processes such as sensory or motor abilities (Matzel et al. 2006). In sum, for rodents, the finding of a first component in cognitive test batteries that corresponds to g is robust, and several implications of its presence have been confirmed. In nonhuman primates, only a handful of studies on the consistency of individual-level differences in cognitive tasks are available. Herndon et al. (1997) were interested in classifying patterns of age-related cognitive decline in adult rhesus macaques, an Old World monkey species. They found a first PCA factor that explained 48% of the variance in cognitive performance and on which all six tasks loaded

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Rodents

Species (n)

Test Battery

Key Findings and Conclusion

Reference

Rats (22 + 201)

4 tasks: attention to novelty, speed, and accuracy of reasoning (8-arm radial maze), response flexibility (detour problem) 5 water escape tasks: route learning (Hebb-Williams maze), use of spatial navigational cues (Morris water maze), spatial reversal learning and visual reversal learning (T-maze), place learning (4-arm maze); plus activity control task

Evidence for g in both samples; g was correlated with brain weight (second sample). Evidence for g in both strains (explaining 61% and 55% of variance in the latency measures, and 28% and 37% in the error measures); authors stress limited implication for g because mainly spatial tasks were used; activity loads on first factor in strain A but not in strain B. Evidence for g (explaining 31% of variance); g was independent of anxiety.

Anderson (1993)

No evidence for g (first factor explains 19.4% of variance, control tasks included in PCA).

Locurto et al. (2003)

Evidence for g (explaining 38% of variance); exploration propensity related to individual learning ability.

Matzel et al. (2003)

Evidence for g (explaining 43% of variance); g covaried with exploration and working memory capacity but not with long-term retention.

Kolata et al. (2005)

Evidence for g (explaining 23%–41% of variance); g showed sibling correlations of 0.17–0.21 and an estimated heritability of 40% (upper limit). Evidence for g (explaining 28%–34% of variance) but only after removing control procedures from the analysis; g was stronger in the second experiment.

Galsworthy et al. (2005)

Evidence for g (explaining 32% of variance); open field exploration and 7 other explorative behaviors also loaded on this first factor, but g was not correlated with general activity, sensory/motor function, physical characteristics, or direct measures of fear; lower-level biological properties loaded weakly and inconsistently on g.

Matzel et al. (2006)

Mice (two strains: 34 + 41)

Mice (40)

Mice (60)

Mice (56)

Mice (21)

Mice (84 unrelated,1 and 167 siblings) Mice (47 + 51)

Mice (43)

6 tasks: curiosity (spontaneous alternation in T-maze), route learning (Hebb-Williams maze), use of spatial navigational cues (Morris water maze), detour problem (burrowing task), contextual memory, plug puzzle; plus anxiety in new environments (open field) 6 tasks: route learning (Hebb-Williams), place learning (plus maze), and a set of detour problems; 3 working memory tasks (8-arm radial maze, 4 × 4 radial maze, visual nonmatching to sample), plus 3 activity and stress control tasks Standard mouse battery of 5 tasks: associative fear conditioning, operant avoidance, path integration (Lashley III maze), odor discrimination, and spatial navigation (spatial water maze) plus open field exploration task Variant of standard mouse battery plus exploration task (open field), long-term retention (retest in Lashley III maze after 30 days) and working memory task (simultaneous performance in two 8-arm radial mazes) Tasks from Galsworthy et al. (2002) plus object exploration and 2nd problem-solving task Exp. 1: 5 tasks: detour, win-shift, olfactory discrimination, fear conditioning, and operant acquisition; plus open field and light-dark control tasks Exp. 2: similar but optimized task battery (same detour and fear conditioning but 3 new tasks, including working memory); same control tasks Standard mouse battery; plus 21 tests of exploratory behavior, sensory/motor function (e.g., running and swimming speed, balance tasks, grip strength) and fitness, emotionality, and hormonal and behavioral stress reactivity

Locurto and Scanlon (1998)

Galsworthy et al. (2002)

Locurto et al. (2006)

(continued)

Burkart et al.: The evolution of general intelligence

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Table 3. Intraspecific studies that have assessed and analyzed correlated performance across at least three cognitive tasks within subjects of the same species, for rodents, primates, and other species

Species (n)

Test Battery

Key Findings and Conclusion

Reference

Mice (27)

Standard mouse battery; plus selective attention (complex discrimination), short-term memory capacity (nonspatial radial arm maze), short-term memory duration (delayed reinforced alternation) Standard mouse battery plus working memory span and capacity, and 12 non-cognitive tests of unlearned behaviors and fitness

Evidence for g (explaining 44% of variance); g was most strongly correlated with selective attention, followed by simple memory capacity and only weakly with short-term memory duration. Evidence for g (explaining 31% of variance); old subjects (19–21 months of age) had lower g than young ones (3–5 months of age) but also showed higher variability. Working memory capacity and duration explained variance in g, and particularly so in old mice. Old mice with age-related cognitive decline had increased body weight and decreased activity. Some non-cognitive variables were also correlated with g. Evidence for g (explaining 27% of variance); exposure to novelty as juveniles (from 39 days of age) and young adults (from 61 days of age) increased exploration but did not affect g compared to control groups when tested as adults (from 79 days of age). Evidence for g (explaining 38% of variation); identification of an additional domain-specific factor for tasks that depended on hippocampal/spatial processing in subsample. Evidence for g (explaining 41%–42% of variance); dopaminergic genes plus one vascular gene significantly correlated with g; D1-mediated dopamine signaling in the prefrontal cortex was predictive of g, arguably through its modulation of working memory. Evidence for g (explaining 30% of variance); working memory training promoted g, largely but not exclusively via increased selective attention; effects were smaller when selective attention load of training task was reduced. Evidence for g (explaining 40% of variance); link between g and exploration propensity was mediated by different rates of habituation in high vs. low g subjects. Evidence for g (explaining 26%–37% of variance); longitudinal working memory training prevented agerelated decline of attention, learning abilities, and cognitive flexibility; non-cognitive variables loaded moderately to weakly on g and in a non-consistent manner; old (from 18 months of age); young (from 5 months of age).

Kolata et al. (2007)

Balb/C Mice (56)

Mice (69)

Standard mouse battery as adults; plus extensive exposure to 12 novel environments prior to testing

Mice (241)

Standard mouse battery; subsample of 78 subjects also tested with 2 additional spatial tasks (win-stay and reinforced alternation)

Mice (60)

Standard mouse battery; plus prefrontal cortex gene expression profiles

Mice (29)

Standard mouse battery; plus extensive training on short-term memory duration and working memory capacity, and a selective attention task (Mouse-Stroop)

Mice (42)

Standard mouse battery: plus 2 exploration tasks (open field and novel environments)

Mice (26)

5 tasks: acquisition of three learning tasks (passive avoidance, shuttle avoidance, reinforced alternation), reversal learning, and selective attention; plus longitudinal working memory training (radial arm maze task with overlapping cues, various regimes) and four non-cognitive variables

Matzel et al. (2008)

Light et al. (2008)

Kolata et al. (2008)

Kolata et al. (2010)

Light et al. (2010)

Light et al. (2011), experiment 2 Matzel et al. (2011a; 2011b)

Burkart et al.: The evolution of general intelligence

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Table 3 (Continued)

Mice (26)

Primates

Rhesus macaques (30+23)

Cotton-top tamarins (22) Chimpanzees (106), 2-year old children (105)

Chimpanzees (99)

Other species

Dogs (13)

Dogs (68 border collies)

Standard mouse battery; plus deductive reasoning (inferring by exclusion: fast mapping) and inductive reasoning (efficient search strategy) 4 learning tasks: odor discrimination, reinforced alternation, fear conditioning, radial arm maze plus attention battery consisting of 4 tasks: Mouse-Stroop (conflicting visual and olfactory cues), T-maze reversal, coupled latent inhibition, and dual radial arm maze 6 non-social tasks (n = 30): delayed non-matching to sample (acquisition time and performance after 120 sec delay), delayed recognition span task (spatial and color condition), and reversal learning task (spatial and object condition) Subset of the 6 tasks above (n = 53): acquisition and 120″ performance in delayed non-matching to sample, spatial delayed recognition span 11 mostly non-social tasks3: 10 from the physical domain, 1 from the social domain 15 of the 16 tasks of the PCTB4 from the physical and social domain (tool use excluded)

13 of the 16 tasks of the PCTB4 from the physical and social domain (without the number addition, social learning, and intention task)

3 tasks: response latencies in discrimination, reversal learning, and visuo-spatial memory (3 delayed non-matching to sample conditions) 6 tasks: four detour tasks, human point following, and numerical discrimination

Evidence for g (explaining 27%–32% of variance); g correlated with inductive and deductive reasoning performance. Evidence for g (explaining 37% of variance); different types of attention (external: selective attention; internal: inhibition) contributed independently to variation in g.

Wass et al. (2012)

Evidence for g (explaining 48% of variance), g but none of the other two extracted factors declined with age. Age groups (age in years): young adults (

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